Temporal Evolution of Trust in Artificial Intelligence-Supported Decision-Making

• Published in: Proceedings of the Human Factors and Ergonomics Society Annual Meeting Vol. 67; no. 1; pp. 1936 - 1941
• Main Authors: Humr, Scott A., Canan, Mustafa, Demir, Mustafa
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01-09-2023
• Subjects: Decision-making; Quantum Cognition; Artificial Intelligence; Human-in-the-loop
• ISSN: 1071-1813; 2169-5067
• DOI: 10.1177/21695067231193672

AI is set to take over some tasks within the decision space that have traditionally been reserved for humans. In return, human decision-makers interacting with AI systems entails rationalization of AI outputs by humans, who may have difficulty forming trust around such AI-generated information. Although a variety of analytical methods have provided some insights into human trust in AI, a more comprehensive understanding of trust may be augmented by generative theories that capture the temporal evolution of trust. Therefore, an open system modeling approach, representing trust as a function of time with a single probability distribution, can potentially improve modeling human trust in an AI system. Results of this study could improve machine behaviors that may help steer a human’s preference to a more Bayesian optimal rationality which is useful in stressful decision-making scenarios.